Methods and systems for automatically determining a neural response threshold current level

ABSTRACT

Methods of automatically determining a neural response threshold current level include identifying one or more neural response signals at one or more corresponding stimulation current levels, identifying one or more non-response signals at one or more corresponding stimulation current levels, and analyzing a trend between the neural response signals and the non-response signals. Systems for automatically determining a neural response threshold current level include one or more devices configured to identify one or more neural response signals at one or more corresponding stimulation current levels, identify one or more non-response signals at one or more corresponding stimulation current levels; and analyze a trend between the neural response signals and the non-response signals.

BACKGROUND

The sense of hearing in human beings involves the use of hair cells inthe cochlea that convert or transduce acoustic signals into auditorynerve impulses. Hearing loss, which may be due to many different causes,is generally of two types: conductive and sensorineural. Conductivehearing loss occurs when the normal mechanical pathways for sound toreach the hair cells in the cochlea are impeded. These sound pathwaysmay be impeded, for example, by damage to the auditory ossicles.Conductive hearing loss may often be helped by the use of conventionalhearing aids that amplify sound so that acoustic signals reach thecochlea and the hair cells. Some types of conductive hearing loss mayalso be treated by surgical procedures.

Sensorineural hearing loss, on the other hand, is due to the absence orthe destruction of the hair cells in the cochlea which are needed totransduce acoustic signals into auditory nerve impulses. Thus, peoplewho suffer from sensorineural hearing loss are unable to derive anybenefit from conventional hearing aid systems.

To overcome sensorineural hearing loss, numerous cochlear implantsystems—or cochlear prosthesis—have been developed. These devices seekto bypass the hair cells in the cochlea by presenting electricalstimulation directly to the auditory nerve fibers. This leads to theperception of sound in the brain and at least partial restoration ofhearing function. To facilitate direct stimulation of the auditory nervefibers, an array of electrodes may be implanted in the cochlea. A soundprocessor processes an incoming sound and translates it into electricalstimulation pulses applied by these electrodes which directly stimulatethe auditory nerve.

Many cochlear implant systems, as well as other types of neuralstimulators, are configured to measure the effectiveness of anelectrical stimulation current applied to neural tissue (e.g., theauditory nerve) by using a process known as neural response imaging(NRI). In NRI, the neural stimulator delivers an electrical stimulus tothe neural tissue with a stimulating electrode and then records theresulting electrical activity of the neural tissue with a recordingelectrode. This resulting electrical activity is often referred to as anevoked neural response and occurs when the neural tissue depolarizes inresponse to the applied stimulus.

An evoked neural response may serve as a diagnostic measure to determinewhether the neural stimulator is functioning correctly. NRI may also beused to determine optimal stimulation parameters for each electrode orelectrode configuration. For example, NRI may be used to determine thelowest level of stimulation current that is required to evoke a neuralresponse in a particular nerve. This information may then be used tooptimize the stimulation parameters or settings of the cochlear implantsystem. NRI may also be used for a number of additional reasons.

In practice, however, the signal recorded by the recording electrodeoften includes undesirable signals that interfere with detection of thedesired neural response. The terms “neural recording” and “neuralrecording signal” will be used herein and in the appended claims, unlessotherwise specifically denoted, to refer to any signal recorded by therecording electrode. As will be explained in more detail below, a neuralrecording signal may include any combination of a neural responsesignal, noise, and/or stimulus artifact. Neural recording signals aresometimes referred to as evoked potential recordings.

As mentioned, a neural recording signal may include noise. Noise refersto any signal that is not correlated with the stimulus that is appliedto the neural tissue by the neural stimulator. Noise is generallyunpredictable.

Furthermore, a neural recording signal may also include stimulusartifact. Stimulus artifact includes signals, other than the neuralresponse, that are correlated with the stimulus that is used to evokethe neural response. For example, the stimulus artifact may include thevoltage potential of the stimulus pulse itself. Another source ofstimulus artifact is cross-talk between the recording circuit and thestimulation circuit.

The presence of noise and stimulus artifact often makes it difficult todetermine whether a neural recording includes a neural response. Anumber of conventional techniques exist for removing noise and stimulusartifact from a signal. However, these techniques are often ineffectivewhen applied to a neural recording signal.

For example, filtering may be used to remove noise that has a differentfrequency than the frequency of a particular signal of interest.However, in neural stimulation systems, the frequency of the noise andthe frequency of an evoked neural response signal are often similar.Thus, conventional filtering cannot always be used to remove noise froma neural recording.

Signal correlation may also be used to remove noise from a signal ofinterest. In signal correlation, a measured signal is correlated with aknown reference signal to remove uncorrelated noise from the measuredsignal. However, evoked neural responses are often variable from patientto patient. Hence, a single reference signal cannot be used to correlateevoked neural responses from multiple patients. The signal correlationtechnique is therefore ineffective in many instances in removing noisefrom a neural recording.

Likewise, a number of conventional techniques exist for removingstimulus artifact from a neural recording. These techniques includealternating polarity, forward masking, third-phase compensation, andscaled template techniques. For example, in the alternating polaritytechnique, the neural response within the neural recording is estimatedto be the average of the responses to a first stimulation pulse having afirst polarity (e.g. cathodic) and a second stimulation pulse having theopposite polarity (e.g. anodic). The neural response stays the samepolarity with the reverse in polarity of the stimulus. However, thestimulus artifact reverses polarity with the reverse in polarity of thestimulus. Consequently, the average response to the two polarities has alower artifact component than either of the responses taken bythemselves. While the alternating polarity technique is sometimessuccessful in reducing stimulus artifact in a neural recording, it doesnot eliminate it in all cases. Furthermore, the alternating polarity, aswell as the other conventional techniques, often leaves large, residualstimulus artifacts in the neural recording.

As mentioned, it is often desirable to determine the minimum stimulationcurrent level needed to evoke a neural response. This minimumstimulation current level is referred to as a “neural response thresholdcurrent level” or simply as a “neural response threshold.” The neuralstimulator may then be configured to apply effective, comfortable, andoptimal stimulus levels that conserve the power available to thestimulator. However, when a neural recording signal is marred by noiseand artifact signals, it is often difficult to visually distinguishbetween a neural recording signal that includes a neural response signaland a neural recording signal that does not include a neural responsesignal. Thus, it is often difficult to determine the neural responsethreshold current level.

SUMMARY

Methods of automatically determining a neural response threshold currentlevel include identifying one or more neural response signals at one ormore corresponding stimulation current levels, identifying one or morenon-response signals at one or more corresponding stimulation currentlevels, and analyzing a trend between the neural response signals andthe non-response signals.

Systems for automatically determining a neural response thresholdcurrent level include one or more devices configured to identify one ormore neural response signals at one or more corresponding stimulationcurrent levels, identify one or more non-response signals at one or morecorresponding stimulation current levels; and analyze a trend betweenthe neural response signals and the non-response signals.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of the presentinvention and are a part of the specification. The illustratedembodiments are merely examples of the present invention and do notlimit the scope of the invention.

FIG. 1 shows a lead having an electrode array with electrodes E1 throughE8 according to principles described herein.

FIG. 2 illustrates an exemplary stimulus that may be delivered to neuraltissue via a stimulating electrode according to principles describedherein.

FIG. 3 shows an exemplary cochlear implant system that may be used as aneural stimulator according to principles described herein.

FIG. 4 is a functional block diagram of an exemplary speech processorand an implantable cochlear stimulator according to principles describedherein.

FIGS. 5A and 5B show a spinal cord stimulator (SCS) system that may beused as a neural stimulator according to principles described herein.

FIG. 6A is a graph depicting an exemplary evoked neural response signalaccording to principles described herein.

FIG. 6B is a graph depicting an exemplary artifact signal according toprinciples described herein.

FIG. 6C is a graph depicting an exemplary neural recording signalaccording to principles described herein.

FIG. 7A is a graph depicting an exemplary noise signal according toprinciples described herein.

FIG. 7B is a graph depicting the effect of the noise signal of FIG. 7Aon the evoked neural response signal of FIG. 6A according to principlesdescribed herein.

FIG. 7C is a graph depicting the effect of the noise signal of FIG. 7Aon the artifact signal of FIG. 6B according to principles describedherein.

FIG. 7D is a graph depicting the effect of the noise signal of FIG. 7Aon the neural recording signal of FIG. 6C according to principlesdescribed herein.

FIG. 8 is a flow chart illustrating an exemplary method of automaticallyidentifying a neural recording signal that includes a neural responsesignal according to principles described herein.

FIG. 9 is a flow chart illustrating an exemplary method of denoising aneural recording signal according to principles described herein.

FIG. 10 is a graph showing the percent of unaccounted variance in amatrix of evoked neural recording signals as a function of number ofcomponents according to principles described herein.

FIG. 11 is a graph illustrating the difference of standard deviations ofthe errors in the beginning versus in the end of the waveforms in thematrix of evoked neural recording signals as a function of the number ofcomponents included according to principles described herein.

FIG. 12 is a graph showing seven basis functions or components accordingto principles described herein.

FIG. 13 is a graph showing the amount by which noise is reduced for eachpoint of the waveform representing the incoming neural recording signalaccording to principles described herein.

FIG. 14 is a graph illustrating the relative contribution of the noiseand the artifact model to the overall uncertainty of the artifact modelaccording to principles described herein.

FIGS. 15A and 15B are graphs illustrating net confidence intervals thatare used to determine whether a neural recording signal includes aneural response signal according to principles described herein.

FIG. 16 is a graph that shows error rates of the automatic neuralresponse identification method when compared to visual identification ofneural response signals by expert medical practitioners for a number ofdifferent threshold values according to principles described herein.

FIG. 17 is a flow chart illustrating an exemplary method ofautomatically determining a neural response threshold current levelaccording to principles described herein.

FIG. 18A shows a number of neural recording signals obtained atdifferent current levels according to principles described herein.

FIG. 18B shows the measurement sequence of the neural recording signalsof FIG. 18A according to principles described herein.

FIG. 19 is a graph illustrating an exemplary analysis of the neuralresponses and the non-responses that may be used to determine the neuralresponse threshold current according to principles described herein.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical, elements.

DETAILED DESCRIPTION

Methods and systems for automatically determining a neural responsethreshold current level are described herein. A number of neuralrecording signals are obtained at different stimulation current levels.A minimum number of these neural recording signals are identified asincluding a neural response signal and a minimum number of these neuralrecording signals are identified as not including a neural responsesignal. The trend of the stimulation current levels corresponding to theidentified signals are analyzed to determine the value of the neuralresponse threshold current.

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present systems and methods. It will be apparent,however, to one skilled in the art that the present systems and methodsmay be practiced without these specific details. Reference in thespecification to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment. Theappearance of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.

FIG. 1 shows a lead (101) supporting an electrode array with electrodesE1 through E8. The lead (101) may be attached to a neural stimulator(not shown). The stimulator is configured to provide an electricalcurrent via the electrode array to stimulate target tissue, e.g., neuraltissue (102). The stimulation current output at each of the electrodes(E1-E8) maybe independently controlled by the stimulator. The lead (101)of FIG. 1 includes eight electrodes for illustrative purposes only. Itwill be recognized that the lead (101) may include any number ofelectrodes. Furthermore, the electrodes may be arranged in any of anumber of configurations. For example, the electrodes may be arranged asan array having at least two or at least four collinear electrodes. Insome embodiments, the electrodes are inductively coupled to thestimulator. The lead (101) may be thin (e.g., less than 3 millimeters indiameter) and flexible such that the lead (101) may be readilypositioned near target neural tissue (102). Alternatively, theelectrodes may be coupled directly to a leadless stimulator.

In some embodiments, each electrode (E1-E8) may be selectivelyconfigured to function as a stimulating electrode or a recordingelectrode as best serves a particular application. For example, E1 maybe a used as a stimulating electrode and E2 may be used as a recordingelectrode. A stimulus, e.g., an electrical stimulation current, may thenbe applied to the neural tissue (102) via the stimulating electrode E1.The resulting electrical activity of the nerve (102) when the nerve(102) depolarizes in response to the applied stimulus is recorded withthe recording electrode E2. As mentioned previously, this electricalactivity is referred to as an evoked neural response or simply a neuralresponse.

FIG. 2 illustrates an exemplary stimulus (120), e.g., an electricalstimulation current pulse, that may be delivered to neural tissue via astimulating electrode. The stimulus (120) of FIG. 2 is biphasic. Inother words, the stimulus (120) includes two parts—a negative firstphase having an area A1 and a positive second phase having an area A2.It is usually the negative phase that causes neural tissue to depolarize(fire). The biphasic stimulus (120) shown in FIG. 2 has an amplitude of1 milliamp (ma) and a pulse width of 20 microseconds (μsec) forillustrative purposes only. It will be recognized that any of thecharacteristics of the stimulus (120), including, but not limited to,the pulse shape, amplitude, pulse width, frequency, burst pattern (e.g.,burst on time and burst off time), duty cycle or burst repeat interval,ramp on time, and ramp off time may vary as best serves a particularapplication.

The biphasic stimulus (120) shown in FIG. 2 is “charge balanced” becausethe negative area A1 is equal to the positive area A2. A charge-balancedbiphasic pulse is often employed as the stimulus to minimize electrodecorrosion and charge build-up which can harm surrounding tissue.However, it will be recognized that the biphasic stimulus (120) mayalternatively be charge-imbalanced as best serves a particularapplication.

In some embodiments, when the amplitude and pulse width of the stimulus(120) of FIG. 2 reach a supra-threshold (i.e., a threshold stimuluslarge enough to depolarize a target nerve), the voltage gradient at somesurface point on the nerve (102; FIG. 1) will be sufficiently negativeas to cause the nerve (102; FIG. 1) to depolarize from its resting stateand propagate an electrical signal along the length of the nerve (102).The voltage gradient of this electrical signal propagation can becaptured with a recording electrode as the evoked neural response of thetarget nerve.

Before discussing the present methods and systems of automaticallydetermining a neural response threshold current, it is helpful tounderstand the components of a number of exemplary neural stimulators inwhich the present methods and systems may be employed.

FIG. 3 shows an exemplary cochlear implant system (20) that may be usedas a neural stimulator in accordance with the present methods andsystems. Exemplary cochlear implant systems suitable for use asdescribed herein include, but are not limited to, those disclosed inU.S. Pat. Nos. 6,219,580; 6,272,382; and 6,308,101, all of which areincorporated herein by reference in their respective entireties. Thecochlear implant system (20) includes a speech processor portion (10)and a cochlear stimulation portion (12). The speech processor portion(10) may include a speech processor (SP) (16), a microphone (18), and/oradditional circuitry as best serves a particular application. Thecochlear stimulation portion (12) may include an implantable cochlearstimulator (ICS) (21), a number of electrodes (50) arranged in anelectrode array (48), and/or additional circuitry as best serves aparticular application. The components within the speech processorportion (10) and the cochlear stimulation portion (12) will be describedin more detail below.

The microphone (18) of FIG. 3 is configured to sense acoustic signalsand convert such sensed signals to corresponding electrical signals. Theelectrical signals are sent to the SP (16) over an electrical or othersuitable link (24). Alternatively, the microphone (18) may be connecteddirectly to, or integrated with, the SP (16). The SP (16) processesthese converted acoustic signals in accordance with a selected speechprocessing strategy to generate appropriate control signals forcontrolling the ICS (21). These control signals may specify or definethe polarity, magnitude, location (i.e., which electrode pair orelectrode group receive the stimulation current), and timing (i.e., whenthe stimulation current is to be applied to a particular electrode pair)of the stimulation current that is generated by the ICS (21).

The electrode array (48) of FIG. 3 is adapted to be inserted within aduct of the cochlea. As shown in FIG. 3, the array (48) includes amultiplicity of electrodes (50), e.g., sixteen electrodes, spaced alongits length. Each of the electrodes (50) is individually connected to theICS (21). The electrode array (48) may be substantially as shown anddescribed in U.S. Pat. Nos. 4,819,647 or 6,129,753, each of which isincorporated herein by reference in its respective entirety. Electroniccircuitry within the ICS (21) is configured to apply stimulation currentto selected pairs or groups of the individual electrodes (50) includedwithin the electrode array (48) in accordance with a specifiedstimulation pattern defined by the SP (16).

The ICS (21) and the SP (16) may be electronically connected via asuitable data or communications link (14). In some embodiments, the SP(16) and the microphone (18) comprise an external portion of thecochlear implant system (20) and the ICS (21) and the electrode array(48) comprise an implantable portion of the system (20). In alternativeembodiments, one or more portions of the SP (16) are included within theimplantable portion of the cochlear implant system (20). The implantableportion of the cochlear implant system (20) is implanted within thepatient's body. Thus, the data link (14) is a transcutaneous (throughthe skin) data link that allows power and control signals to be sentfrom the SP (16) to the ICS (21). In some embodiments, data and statussignals may also be sent from the ICS (21) to the SP (16).

The external and implantable portions of the cochlear implant system(20) may each include one or more coils configured to transmit andreceive power and/or control signals via the data link (14). Forexample, the external portion of the cochlear implant system (20) mayinclude an external coil (not shown) and the implantable portion of thecochlear implant system (20) may include an implantable coil (notshown). The external coil and the implantable coil may be inductivelycoupled to each other, thereby allowing data to be transmitted betweenthe external portion and the implantable portion. The data may include,for example, the magnitude and polarity of a sensed acoustic signal. Theexternal coil may also transmit power from the external portion to theimplantable portion of the cochlear implant system (20). It will benoted that, in some embodiments, both the SP (16) and the ICS (21) maybe implanted within the patient, either in the same housing or inseparate housings. If the SP (16) and the ICS (21) are in the samehousing, the link (14) may be realized with a direct wire connectionwithin such housing. If the SP (16) and the ICS (21) are in separatehousings, the link (14) may be an inductive link, for example.

FIG. 4 is a functional block diagram of an exemplary SP (16) and ICS(21). The functions shown in FIG. 4 are merely representative of themany different functions that may be performed by the SP (16) and/or theICS (21). A more complete description of the functional block diagram ofthe SP (16) and the ICS (21) is found in U.S. Pat. No. 6,219,580, whichis incorporated herein by reference in its entirety.

As shown in FIG. 4, the microphone (18) senses acoustic information,such as speech and music, and converts the acoustic information into oneor more electrical signals. These signals are then amplified in audiofront-end (AFE) circuitry (22). The amplified audio signal is thenconverted to a digital signal by an analog-to-digital (A/D) converter(28). The resulting digital signal is then subjected to automatic gaincontrol using a suitable automatic gain control (AGC) function (29).

After appropriate automatic gain control, the digital signal is thenprocessed in one of a number of digital signal processing or analysischannels (44). For example, the SP (16) may include, but is not limitedto, eight analysis channels (44). Each analysis channel (44) may respondto a different frequency content of the sensed acoustical signal. Inother words, each analysis channel (44) includes a band-pass filter(BP1-BPFn) or other type of filter such that the digital signal isdivided into n frequency channels. The lowest frequency filter may be alow-pass filter, and the highest frequency filter may be a high-passfilter.

As shown in FIG. 4, each analysis channel (44) may also include adetection stage (D1-Dn). Each detection stage (D1-Dn) may include anenergy detection circuit (not shown), which may be realized, e.g.,through a rectification circuit followed by an integrator circuit. Asshown in FIG. 4, each of the detection stages (D1-Dn) may alternativelybe bypassed depending on the particular signal processing strategy beingused.

After energy detection, or bypassing of such, the signal from each ofthe n analysis channels (44) is forwarded to a mapping stage (41). Themapping stage (41) may be configured to map the signals in each of theanalysis channels (44) to one or more of the m stimulus channels (46).The mapping stage (41) may be further configured to perform additionalprocessing of the signal, such as signal compression. The signals outputby each analysis channel (44) may then be serialized by a multiplexer(45) into one serial data channel. The multiplexed signal may then befurther processed according to information included in a pulse table(42) connected to an arithmetic logic unit (ALU) (43). After the signalis appropriately processed, compressed, and mapped, the signal may beinput into the ICS (21) to control the actual stimulus patterns that areapplied to the patient via the electrode array (48; FIG. 3).

As mentioned, each of the n analysis channels (44) may be mapped to oneor more stimulus channels (46). In other words, the informationcontained in the n analysis channels (44) controls the stimulus patternsthat are applied to the patient by the ICS (21) and its associatedelectrode array (48; FIG. 3). Stimulus current may be applied to anynumber of stimulation sites within the patient's cochlea via the mstimulus channels (46). As used herein and in the appended claims, theterm “stimulation site” will be used to refer to a target area orlocation at which the stimulus current is applied. For example, astimulation site may refer to a particular location in the neural tissueof a cochlear implant patient. Through appropriate weighting and sharingof currents between the electrodes (50; FIG. 3), stimulus current may beapplied to any stimulation site along the length of the electrode array(48; FIG. 3).

FIGS. 5A and 5B show a spinal cord stimulator (SCS) system (110) thatmay be used as a neural stimulator in accordance with the presentmethods and systems. The SCS (110) may be used to treat a number ofdifferent medical conditions such as, but not limited to, chronic pain.

As shown in FIG. 5A, the SCS (110) may include an implantable pulsegenerator (IPG) (112), a lead extension (114), and an electrode lead(116) having an electrode array (118) thereon. The electrode array (118)includes a plurality of electrodes (117). The electrodes (117) may bearranged, as shown in FIG. 5A, in an in-line array near the distal endof the lead (116). Other electrode array configurations may also beused. The lead extension (114) need not always be used with the SCS(110), but may be used depending on the physical distance between theIPG (112) and the stimulation site within the patient. The IPG (112) isconfigured to generate stimulation current pulses that are applied to astimulation site via one or more of the electrodes (117). Exemplaryspinal cord stimulators suitable for use as described herein include,but are not limited to, those disclosed in U.S. Pat. Nos. 5,501,703;6,487,446; and 6,516,227, all of which are incorporated herein byreference in their respective entireties.

FIG. 5B shows that the electrode array (118) of the SCS (110) may beimplanted in the epidural space (120) of a patient in close proximity tothe spinal cord (119). Because of the lack of space near the lead exitpoint (115) where the electrode lead (116) exits the spinal column, theIPG (112) is generally implanted in the abdomen or above the buttocks.However, it will be recognized that the IPG (112) may be implanted inany suitable implantation site. The lead extension (114) facilitatesimplanting the IPG (112) at a location that is relatively distant fromthe lead exit point (115).

The cochlear implant system (20; FIG. 3) and the SCS (110; FIG. 5A) aremerely illustrative of many types of neural stimulators that may be usedto perform NRI. For example, the neural stimulator may additionally oralternatively include an implantable pulse generator (IPG) coupled toone or more leads having a number of electrodes, a deep brainstimulator, an implantable microstimulator, an external stimulator, orany other type of stimulator configured to perform NRI. Exemplary IPGssuitable for use as described herein include, but are not limited to,those disclosed in U.S. Pat. Nos. 6,381,496, 6,553,263; and 6,760,626.Exemplary deep brain stimulators suitable for use as described hereininclude, but are not limited to, those disclosed in U.S. Pat. Nos.5,938,688; 6,016,449; and 6,539,263. Exemplary implantablemicrostimulators, such as the BION® microstimulator (Advanced Bionics®Corporation, Valencia, Calif.), suitable for use as described hereininclude, but are not limited to, those disclosed in U.S. Pat. Nos.5,193,539; 5,193,540; 5,312,439; 6,185,452; 6,164,284; 6,208,894; and6,051,017. All of these listed patents are incorporated herein byreference in their respective entireties.

As mentioned, it is often desirable to deliver a stimulus to neuraltissue with a stimulating electrode and then record the resultingelectrical activity of the neural tissue with a recording electrode.This resulting electrical activity is referred to as an evoked neuralresponse or simply, a neural response, and occurs when the neural tissuedepolarizes in response to the applied stimulus.

For example, in a normal ear, a single auditory nerve fiber or cellgenerates an action potential when the cell's membrane is depolarized toa threshold value, after which a spike occurs. Sodium ions entering thecell make the inside of the cell more positive, that is, depolarized. Insome embodiments, an electrical stimulation current may be used todepolarize the nerve cell. This depolarization effect can be likened totaking a photograph by pressing the shutter button on a camera. Pressingon the button has no effect until it crosses a threshold pressure, andthen “click”—the shutter opens and the film is exposed. In the same way,depolarizing a neuron has no effect until the depolarization reaches athreshold, and then, all at once, an action potential is generated.

The evoked neural response as recorded by the recording electrodeincludes a sum of action potentials of a number of nerve cells. FIG. 6Ais a graph depicting an exemplary evoked neural response signal (160).As shown in FIG. 6A, the horizontal axis represents time in samples andthe vertical axis represents the amplitude of the response in microvolts(μV). As shown in FIG. 6A, the evoked neural response signal (160) istypically characterized by a first negative peak (N1) followed by afirst positive peak (P1). It will be recognized that evoked neuralresponses differ in timing and amplitude from patient to patient.

Unfortunately, the recording electrode may additionally or alternativelyrecord noise and/or stimulus artifact. In general, a neural recordingmay include any combination of a neural response signal, noise, and/orstimulus artifact. In some instances, the neural recording obtained bythe recording electrode only includes stimulus artifact and noise. Forexample, if the stimulus pulse is too low to trigger depolarization ofthe nerve (102), the nerve (102) will not produce a neural response andthe recording electrode will only record the stimulus artifact and anynoise that is present.

FIG. 6B is a graph depicting an exemplary artifact signal (161). Theartifact signal. (161) is typically characterized as a sum of twodecaying exponentials, one with a fast time constant and one with a slowtime constant.

FIG. 6C is a graph depicting a neural recording signal (162) thatincludes both the evoked neural response signal (160) of FIG. 6A and theartifact signal (161) of FIG. 6B. As shown in FIG. 6C, the neuralrecording signal (162) is a sum of the evoked neural response signal(160; FIG. 6A) and the artifact signal (161; FIG. 6B).

As mentioned previously, the neural recording signal obtained by arecording electrode may also include noise. Noise refers to any signalthat is not correlated with the stimulus pulse and is generallyunpredictable. FIG. 7A is a graph depicting an exemplary noise signal(170) that may be recorded by the recording electrode. Because the noisesignal (170) is unpredictable, the noise signal (170) may have anyfrequency or amplitude.

FIGS. 7B-7D are graphs depicting the effect (171) of the noise signal(170; FIG. 7A) on the evoked neural response signal (160) of FIG. 6A,the effect (172) of the noise signal (170; FIG. 7A) on the artifactsignal (161) of FIG. 6B, and the effect (173) of the noise signal (170;FIG. 7A) on the neural recording signal (162) of FIG. 6C, respectively.

It is often desirable to determine whether a neural recording signalincludes a neural response signal or whether the neural recording signalonly includes noise and/or artifact signals. Currently, medicalpractitioners typically need to be trained to identify signals ascontaining valid neural responses from a visual display of the neuralrecording signal. For example, a typical neural response signal of theauditory nerve to a stimulus pulse includes a negative peak followed bya positive peak, such as the signal (160) shown in FIG. 6A. Waveformsthat do not fall into this pattern are assumed to be recordings thatcontain only noise and/or stimulus artifact. However, because medicalpractitioners may have various degrees of training, the results ofidentifying signals containing valid neural responses may vary greatlyfrom one practitioner to the next. In addition, some valid neuralresponses do not follow typical neural response patterns. Whether theseresponses will be correctly identified as valid neural responses dependson the judgment of the practitioner.

To overcome the inaccuracies of practitioner identification of validneural responses and to improve NRI performance, the identification of aneural recording signal that includes a neural response signal may beautomated. FIG. 8 is a flow chart illustrating an exemplary method ofautomatically identifying a neural recording signal that includes aneural response signal. The method described in connection with FIG. 8is more fully described in a related application entitled “Methods andSystems for Automatically Identifying a Neural Recording Signal asIncluding a Neural Response Signal” to Litvak et al., client docketnumber AB-613U, which application was filed simultaneously with thepresent application on Jun. 1, 2005. The AB-613U application isincorporated herein by reference in its entirety.

The method described in connection with FIG. 8 may be used in connectionwith any type of neural stimulator. Furthermore, the steps shown in FIG.8 and described below may be modified, reordered, removed, and/or addedto as best serves a particular application. It will be recognized that acomputer, digital signal processor (DSP), mathematical application, orany other suitable device, signal processor, software, firmware, orapplication may be used to implement one or more of the steps describedin connection with FIG. 8.

As shown in FIG. 8, a neural recording signal is first obtained (step210). As described above, the neural recording signal may be obtained bystimulating neural tissue with a stimulating electrode and thenrecording the electrical response of the neural tissue with a recordingelectrode. It will be recognized that the neural recording signal may beevoked in response to stimulus applied to any neural tissue by anyneural stimulator. For example, the neural recording signal may capturea neural response evoked by a stimulus applied to the auditory nervewith a cochlear implant system.

Once the neural recording signal has been obtained (step 210), theneural recording signal is conditioned (step 211). In some embodiments,the neural recording signal is conditioned by removing the mean of thedata within the neural recording, removing a trend from the data, and/orremoving an overall DC voltage level from the data. The neural recordingsignal may additionally or alternatively be conditioned using any othersuitable conditioning technique.

The noise that is present in the neural recording signal is thenestimated (step 212). A number of different techniques may be used toestimate the nose in the neural recording signal. For example, the noisemay be estimated by computing the standard deviation of the data nearthe tail of the neural recording signal. Alternatively, the noise may bedirectly estimated by analyzing variability between a number ofdifferent neural recording signals that are obtained.

The neural recording signal is then denoised (step 213). The neuralrecording signal may be denoised using any of a number of differenttechniques. For example, the neural recording signal may be denoised byapplying principle component analysis, as is more fully described in arelated application entitled “Methods and Systems for Denoising a NeuralRecording Signal” to Litvak et al., client docket number AB-611U, whichapplication was filed simultaneously with the present application onJun. 1, 2005. The AB-611U application is incorporated herein byreference in its entirety.

An exemplary method of denoising a neural recording signal by applyingprincipal component analysis will now be described in connection withthe flow chart shown in FIG. 9. The term “denoising” will be used hereinand in the appended claims, unless otherwise specifically denoted, torefer to decreasing or removing noise from a neural recording signal orany other signal as best serves a particular application. The method maybe used in connection with any type of neural stimulator. The stepsshown in FIG. 9 and described below may be modified, reordered, removed,and/or added to as best serves a particular application.

As shown in FIG. 9, a number of basis functions are first derived usingprincipal component analysis to describe a set of previously collectedneural recording signals (step 180). Principal component analysis is astatistical technique used to derive a number of functions that, whensummed together, describe a given set of data. These functions are oftenreferred to as basis functions or principal components, both of whichterms will be used interchangeably herein and in the appended claimsunless otherwise specifically denoted.

An example of deriving a number of basis functions that describe a setof neural recording signals corresponding to the auditory nerve will nowbe given. It will be recognized that the following example is merelyillustrative and that the neural recording signal may be evoked inresponse to stimulus applied to any neural tissue by any neuralstimulator.

A large number of neural recording signals were evoked and recorded byaudiologists over a period of time. Each measured waveform was computedby averaging the response to a cathodic-anodic and anodic-cathodicstimulus pulse. A two-point averaging filter was then applied to thedata. In addition, synchronized noise was measured by recording theresponse to stimulation with zero current. The synchronized noise wasthen subtracted from the response to the cathodic-anodic andanodic-cathodic stimulus pulse.

The evoked neural recording signals were then collected into ameasurement matrix M=[m₁ . . . m₈₀₀₀]. As used herein and in theappended claims, unless otherwise specifically denoted, bold capitalletters will be used to refer to matrices and bold lower-case letterswill be used to refer to vectors. Hence, M is a matrix containing 8,000measured neural recording signals m₁ through m₈₀₀₀. Although M contains8,000 measured neural recording signals in the present example, it willbe recognized that M may contain any number of measured neural recordingsignals as best serves a particular application.

Eigenvalue decomposition was then used to compute the principalcomponents of M. MATLAB™ or any other mathematical tool may be used toperform the eigenvalue decomposition. First, the covariance matrixC_(M)=COV(M′) was computed. A vector of eigenvalues (λ) and a matrix ofeigenvectors arranged in columns (V_(full)) were then computed. Thematrix V_(full) contains the full components that account entirely forthe measurement matrix M.

Because the covariance matrix C_(M) is symmetric, the eigenvectorswithin the matrix V_(full) are orthogonal. The eigenvectors within thematrix V_(full) may be normalized to have a norm of one.

Although V_(full) contains the full components that account entirely forthe data contained in measurement matrix M, it can be shown that alesser number of these components may sufficiently account for the datain M. FIG. 10 is a graph showing the percent of unaccounted variance inM as a function of the number of components. As shown in FIG. 10, thepercent of unaccounted variance decreases as more components areincluded. However, as shown in FIG. 10, a small number of components(e.g., 5 to 10 components) may account for approximately 98 to 99percent of the variance.

FIG. 11 is a graph illustrating the difference of standard deviations ofthe errors in the beginning versus in the end of the waveforms in M as afunction of the number of components included. The error bars (e.g.,190) are approximately 99 percent confidence intervals around the meanestimate of the error. As shown in FIG. 11, the difference becomes zerofor eight components. For higher numbers of components, some noise iscaptured in the measurements. Hence, the error in the beginning portionof the stimulus is less than the standard of deviation.

The results shown in FIGS. 10 and 11 may be used to determine an optimalnumber of basis functions or components for a given application. Forexample, seven components capture approximately 98.6 percent of thevariance in the data and have a 2 μV mean difference. Thus, sevencomponents are sufficient for many different applications. The examplesgiven herein will use seven components or basis functions. However, itwill be recognized that any number of basis functions may be chosen torepresent the set of evoked neural recording signals in M.

FIG. 12 is a graph showing seven basis functions or components. As shownin FIG. 12, the top basis function (basis function number 7) looks likea neural response signal. The remaining basis functions account fordifferences in the evoked neural recording signals in M. For purposes ofthe present example, the seven basis functions or components will berepresented by the component matrix V=[v₁ . . . v₇], where v₁ through v₇are vectors representing the seven basis functions. As will be describedin more detail below, the component matrix V may be used to denoise anincoming neural recording signal.

Returning to the flow chart of FIG. 9, once the component matrix V hasbeen determined, the next step is to determine relative weights for thebasis functions v₁ through v₇ corresponding to an incoming neuralrecording signal (step 181). In other words, the amount of each basisfunction v₁ through v₇ that is present in the incoming neural recordingsignal is determined. A computer, digital signal processor (DSP), or anyother suitable device or application may be used to determine therelative weights for the basis functions. As will be described in moredetail below, the incoming neural recording signal is denoised bymultiplying the weights with the basis functions v₁ through v₇.

For example, assume that the incoming neural recording signal isrepresented by m. The relative weights for the basis functions v₁through v₇ are determined by correlating the incoming neural recordingsignal m with the basis functions in the component vector V. Hence, theweights are equal to V′ m.

As shown in FIG. 9, the weights are then multiplied with the basisfunctions to denoise the incoming neural recording signal (step 182).Thus, the denoised neural recording signal, m_(denoised), is equal to VV′ m. For ease of explanation, m_(denoised)=T m, where T is thedenoising matrix equal to V V′. A computer, digital signal processor(DSP), or any other suitable device or application may be used toresynthesize the weights.

Mathematically, the denoising effect of multiplying the weights with thebasis functions can be shown by the following equations. Suppose thatthe incoming neural recording signal is m=s+n, where s represents theevoked neural response signal and/or artifact signal and n representsthe uncorrelated noise. Without loss of generality, it can be assumedthat n has a zero mean. The denoised waveform is then m_(denoised)=T m=Ts+T n=s_(denoised)+T n. Therefore, the uncorrelated noise in thedenoised waveform is n_(denoised)=m_(denoised)−s_(denoised)=T n.

Conceptually, the denoising effect of multiplying the weights with thebasis functions can be illustrated by the following example. Supposethat there is only one basis function and the incoming neural recordingonly contains noise. When this incoming noise is correlated with thebasis function, the resulting weight value is low, indicating that thenoise does not correlate with the basis function. When the low weightnumber is multiplied with the basis function, the resulting signal ischaracterized by a smaller magnitude than the incoming noise signal.

On the other hand, suppose that the incoming neural recording isnoiseless. Therefore, when the incoming neural recording signal iscorrelated with the single basis function, the resulting weight numberis high, indicating that the incoming neural recording signal correlateswith the basis function. When the high weight number is multiplied withthe basis function, the resulting signal is characterized by a magnitudethat is relatively close to the magnitude of the incoming neuralrecording signal.

The noise can be described by the covariance matrix CD_(n)=E[n n′]=T E[nn′]T′=T C_(n) T′. The diagonal of the matrix CD_(n) is the variance atany point. Therefore, the square root of the diagonal is equal to thestandard deviation at any given point. Assuming that the incoming noiseis white, with unity variance, the decrease in the noise standarddeviation is shown in FIG. 13. FIG. 13 shows, as a function of samplenumber, the amount by which noise is reduced for each point of thewaveform representing the neural recording. The horizontal line (193)represents the noise level of the incoming neural recording beforedenoising. The line (192) represents the noise level of the incomingneural recording signal after denoising. The shaded area represents therange of time where most of the response energy is maximal. In thisarea, as shown in FIG. 13, an average reduction in noise of nearly 50percent is achieved by the denoising technique described herein.

In some embodiments, greater noise reductions may be achieved byincluding fewer components. However, the cost of including fewercomponents may be loss of some energy in the denoised signal.

Returning to the flow chart of FIG. 8, after the neural recording signalhas been denoised (step 213), confidence intervals corresponding to theneural recording signal may be determined (step 214). The confidenceintervals take into account the uncertainty in the denoised neuralresponse signal. The confidence intervals may be derived from anycombination of a number of contributing factors including, but notlimited to, estimates of noise levels, relative noise levels before andafter multiplying the weights with the basis functions, and otherfactors.

As shown in FIG. 8, the method also includes fitting an artifact modelto the obtained neural recording signal (step 216). The artifact modeldescribes a typical or model stimulus artifact signal, and, as will bedescribed in more detail below, may be used to determine whether aneural recording signal includes a neural response signal.

As used herein and in the appended claims, unless otherwise specificallydenoted, the variable a_(m)(t) will be used to represent an artifactmodel. As mentioned, a stimulus artifact signal can be characterized asa sum of two decaying exponentials. Hence, the artifact model may bedescribed by the following equation: a_(m)(t)=A₁·e^(−α·t)+B·e^(−β·t),where α and β are time constants. Since the time constant β is largecompared to the time scale of interest, the second exponential in thisequation can be estimated by a linear trend. Hence,a_(m)(t)=A₁·e^(−α·t)+A₂·t+A₃. All of the parameters in this model arelinear, except for the coefficient α. As will be described in moredetail below, the values of the parameters [α, A₁, A₂, A₃] may beadjusted to fit the artifact model to a neural recording signal.

The variable m(t) will be used herein and in the appended claims, unlessotherwise specifically denoted, to represent a neural recording signal.Hence, m(t)=a(t)+s(t)+n(t), where a(t) represents the stimulus artifactsignal, s(t) represents the neural response signal, and n(t) representsthe noise signal. To fit the artifact model a_(m)(t) to the neuralrecording signal m(t), the model parameters [α, A₁, A₂, A₃] aredetermined for which the error between the artifact model and the datawithin the neural recording signal is minimized. Heuristic optimizationsmay be applied to limit the artifact model. For example, the parameterA₁ may be required to have a positive value. A computer, digital signalprocessor (DSP), mathematical application, or any other suitable deviceor application may be used to fit the artifact model a_(m)(t) to theneural recording signal m(t).

Once the artifact model has been fitted to the neural recording signal(step 216), the fitted artifact model signal is denoised (step 217). Thefitted artifact model is denoised to eliminate or reduce distortions oruncertainties in the model due to the noise that is present in theneural recording signal. The fitted artifact model signal may bedenoised using principal component analysis, as described above, or byusing any other suitable denoising technique.

After the fitted artifact model signal has been denoised (step 217),confidence intervals for the fitted artifact model signal are determined(step 218). These confidence intervals are determined by a number ofuncertainties in the artifact parameters given the noise level in theneural recording signal. For example, there may be uncertainty in thestimulus, uncertainty in the model, and uncertainty in the noise. FIG.14 is a graph illustrating the relative contribution of the noise (230)and the artifact model (231) to the overall uncertainty of the fittedartifact model signal. The results in FIG. 14 and/or additional oralternative factors may be used in determining the confidence intervalsfor the fitted artifact model signal.

Returning to FIG. 8, once the confidence intervals have been determinedfor the neural recording signal (step 214) and for the fitted artifactmodel signal (step 218), net or total confidence intervals are computedby summing the neural recording signal confidence intervals and fittedartifact model signal confidence intervals (step 219). FIGS. 15A and 15Bshow exemplary net confidence intervals (240). As will be described inmore detail below, these net confidence intervals (240) are used todetermine whether a neural recording signal includes a neural responsesignal.

Returning to FIG. 8, after the net confidence intervals have beencomputed (step 219), a strength-of-response (SOR) metric correspondingto the observed neural recording signal is computed (step 220). The SORmetric describes the distance of the fitted artifact model signal to theobserved neural recording signal relative to the net confidenceintervals (240). A neural recording signal may be identified asincluding a neural response signal if the SOR metric exceeds apre-determined threshold.

The SOR metric may be any metric that describes the distance of thefitted artifact model signal to the observed neural recording signalrelative to the net confidence intervals (240). A number of differentSOR metrics may be used. One exemplary SOR metric is

${{SOR} = \sqrt[6]{\frac{1}{35}{\sum\limits_{t \in {\lbrack{22,27}\rbrack}}^{\;}\left( \frac{\left( {{\overset{\_}{m}(t)} - {{\overset{\_}{a}}_{m}(t)}} \right)}{c(t)} \right)^{6}}}},$where c(t) is the net confidence interval size. This equation may bemodified as best serves a particular application.

The size of the SOR metric is then evaluated (step 221) to determinewhether the neural recording signal includes a neural response signal orwhether the neural recording signal only includes noise and artifactsignals. The SOR metric evaluation may be performed automatically with acomputer, DSP, mathematical application, or any other suitable device orapplication. If the SOR metric exceeds a pre-determined SOR thresholdvalue, the neural recording signal is identified as including a neuralresponse signal (222). Conversely, if the SOR metric is below thepre-determined SOR threshold, the neural recording signal is identifiedas not including a neural response signal (step 224).

Additionally or alternatively, further neural recording signals may beobtained and averaged (step 223) if the SOR metric is too close to theSOR threshold to accurately identify as corresponding to a neuralrecording signal that includes a neural response signal or not. A newSOR metric may be computed and evaluated for these additional neuralrecording signals.

An example of determining whether a neural recording signal includes aneural response signal by evaluating the SOR metric will be described inconnection with FIGS. 15A and 15B. FIG. 15A shows a first exemplaryneural recording signal (242) and a corresponding denoised neuralrecording signal (241) that has been fitted by the artifact model. Asshown in FIG. 15A, the denoised recording signal (241) is relativelyclose to the confidence interval (240) and may therefore be difficult tovisually identify as including a neural response signal. However,suppose that the pre-determined SOR threshold is m=35. Using the SORmetric equation shown above, the SOR metric for the denoised recordingsignal (241) is equal to m=39.059, well above the SOR threshold value of35. Therefore, the neural recording signal (242) may be identified asincluding a neural response signal.

FIG. 15B shows a second exemplary neural recording signal (244) and itscorresponding denoised neural recording signal (243) that has beenfitted by the artifact model. The SOR metric for this neural recordingsignal (244) is equal to m=20.2673, well below the SOR threshold valueof 35. Therefore, the neural recording signal (244) may be identified asnot including a neural response signal.

An optimal threshold value may be determined using a number of differenttechniques. In some embodiments, the optimal threshold value isdetermined by comparing the results of the automatic neural responseidentification method of FIG. 8 to the results of visual identificationof the same neural response signals by expert medical practitioners. Forexample, FIG. 16 is a graph that shows error rates of the automaticneural response identification method when compared to visualidentification of neural response signals by expert medicalpractitioners for a number of different threshold values. Curve (250)shows the percentage of “false positives” (i.e., the percentage ofneural recording signals falsely identified as including a neuralresponse signal) per threshold value, curve (251) shows the percentageof “false negatives” (i.e., the percentage of neural recording signalsfalsely identified as not including a neural response signal) perthreshold value, and curve (252) shows the net error rate per thresholdvalue. The optimal threshold value is determined by choosing thethreshold value that corresponds to the minimum value of the net errorrate curve (252). Hence, the optimal threshold value for the curvesshown in FIG. 16 is approximately equal to 35.

As mentioned, it is often desirable to determine the minimum stimulationcurrent level needed to evoke a neural response, i.e., the neuralresponse threshold current level. However, noise and artifact signalscontained in a neural recording signal often make it difficult todetermine the neural response threshold current level.

Hence, an exemplary method of automatically determining a neuralresponse threshold current level will now be described in connectionwith the flow chart of FIG. 17. The steps shown in FIG. 17 and describedbelow may be modified, reordered, removed, and/or added to as bestserves a particular application. Furthermore, it will be recognized thata computer, digital signal processor (DSP), mathematical application, orany other suitable device, signal processor, software, firmware, orapplication may be used to implement one or more of the steps describedin connection with FIG. 17.

The exemplary method shown in FIG. 17 includes identifying a number ofneural recording signals at different stimulation current levels thatmost likely include neural response signals and a number of neuralrecording signals at different stimulation current levels that mostlikely do not include neural response signals. The neural responsethreshold current level may then be determined by analyzing theamplitudes of the neural recording signals and their correspondingstimulation current levels.

As shown in FIG. 17, a neural recording signal is first obtained withA_(step) averages (step 300). In other words, the neural recordingsignal is the average of A_(step) neural recordings at a particularstimulation current level. The value of A_(step) may vary as best servesa particular application. An exemplary, but not exclusive, value forA_(step) is 32.

An SOR metric is then computed for the obtained neural recording signal(step 301). The SOR metric may be computed using the method alreadydescribed in connection with FIG. 8.

The computed SOR metric is then compared against an SOR threshold valueto determine whether the neural recording signal includes a neuralresponse signal. As used herein and in the appended claims, unlessotherwise specifically denoted, the variable SOR_(crit) will be used torepresent the SOR threshold value. As described previously, if the SORmetric is too close to the SOR threshold, the probability of falselyidentifying the neural recording signal as including a neural responsesignal or not is greatly increased. Hence, in step 302, the SOR metricis compared against SOR_(min) and SOR_(max), upper and lower uncertaintylimits, respectively, that surround the SOR threshold value. The valuesfor SOR_(crit), SOR_(min), and SOR_(max) may vary as best serves aparticular application. Exemplary values obtained using the equation

${{SOR} = \sqrt[6]{\frac{1}{35}{\sum\limits_{t \in {\lbrack{22,27}\rbrack}}^{\;}\left( \frac{\left( {{\overset{\_}{m}(t)} - {{\overset{\_}{a}}_{m}(t)}} \right)}{c(t)} \right)^{6}}}},$as described above, may be SOR_(crit)=32, SOR_(min)=27, andSOR_(max)=35.

If the SOR metric value falls within these uncertainty limits (Yes; step302), the SOR metric is too close to the SOR threshold to accuratelyidentify the neural recording signal as having captured an actual neuralresponse. Averaging in additional neural recording signals may reducethe effect of the noise and artifact signals enough to make a moreaccurate identification of the averaged neural recording signal. Hence,the method next determines whether additional neural recording signalsmay be obtained at the same stimulation current level to include in theaverage (step 303). If additional neural recording signals may beobtained at the same stimulation current level (Yes; step 303), themethod returns to step 300 wherein additional neural recording signalsare obtained and averaged.

In some instances, however, additional neural recording signals may notbe obtained for inclusion in the average being calculated (No; step303). For example, a medical practitioner may determine that additionaltime at the present stimulation current level may be harmful to thepatient. Alternatively, the practitioner may have only a limited timefor making the measurement. Hence, a maximum number of averages, A_(max)at the present stimulation current level may be specified by the medicalpractitioner. The total number of signals recorded and averaged togetherat a specific current level may not exceed A_(max). If it is determinedthat no additional signals may be obtained (No; step 303), the methodproceeds to step 304, which will be described in more detail below.

Returning to step 302, if the SOR metric value does not fall withinSOR_(min) and SOR_(max) (No; step 302), an accurate identification ofthe neural recording signal may be made. If the SOR metric is greaterthan SOR_(crit), the neural recording signal is identified as includinga neural response signal and the total number of neural responses R thathave been identified is incremented by one (step 304). However, if theSOR metric is less than SOR_(crit), the neural recording signal isidentified as not including a neural response signal and the totalnumber “non-responses” NR that have been identified is incremented byone (step 305). As used herein and in the appended claims, unlessotherwise specifically denoted, the term “non-response” and“non-response signal” will be used interchangeably to refer to a neuralrecording signal that does not include a neural response signal.

If there has not yet been a neural response R identified (No; step 306),it is next determined whether the stimulation current level can beincreased (step 309). If the stimulation current level can be increased(Yes; step 309), the stimulation current level is increased from thehighest previously-tried current level (step 311). New neural recordingsmay then be obtained at this higher current level (step 300). Thisprocess may be repeated until a neural stimulus R is identified (Yes;step 306).

However, in some instances, the stimulation current level cannot beincreased (No; step 309). For example, a medical practitioner maystipulate that the stimulation current level cannot go above aparticular maximum level. In these instances, more neural recordingsignals may be obtained and averaged at the current stimulation currentlevel if the maximum number of allowable signals at that stimulationcurrent level has not been exceeded (Yes; step 310). As mentioned above,collecting and averaging additional signals may enable a more accurateidentification of an actual neural response. If more signals for theaverage cannot be obtained at the maximum current level (No; step 310),the method may end without identifying any neural responses R. In such acase, an option may be presented to the medical practitioner to obtainmore neural recording signals at a higher stimulation current leveland/or with a different recording electrode.

As shown in FIG. 17, once a neural response R has been identified (step306), the method determines whether a minimum number of non-responsesNR_(min) has been obtained (step 307). NR_(min) may vary as best servesa particular application. For example, in some embodiments, NR_(min) istwo. If the minimum number of non-responses NR_(min) has not beenobtained (No; step 307), the stimulation current level (step 312) may begradually decreased until the minimum number of non-responses NR_(min)are recorded (Yes; step 307). NR_(min) may vary as best serves aparticular application. For example, in some embodiments, NR_(min) istwo.

Next, the stimulation current level may be increased (step 311) until aminimum number of responses are measured (Yes; step 308). R_(min) mayvary as best serves a particular application. For example, in someembodiments, R_(min) is four. The neural response threshold currentlevel may more accurately be determined with higher values of R_(min).

It will be recognized that the order in which the neural responses R andthe non-responses NR are obtained may vary as best serves a particularapplication. For example, the non-responses NR may be obtained first.The stimulation current may then be increased to obtain the neuralresponses R. In any case, the identification of a minimum number ofresponses and non-responses increases the accuracy and confidence in theidentified neural response threshold.

In some instances, a stray measurement may be obtained. For example, aneural response R may be obtained at a stimulation current level that isin between stimulation current levels corresponding to two non-responsesNR or vice versa. These stray measurements may be ignored or otherwisedealt with as best serves a particular application.

The method of obtaining a number of neural responses R_(min) andnon-responses NR_(min) will be illustrated in connection with FIGS. 18Aand 18B. For illustrative purposes only, both R_(min) and NR_(min) areequal to four. FIG. 18A shows a number of neural recording signals(320-328) obtained at different current levels. Confidence intervals(e.g., 330) corresponding to each neural recording signal (320-328) arealso shown.

FIG. 18B shows the measurement sequence of the neural recording signals(320-328) of FIG. 18A. As shown in FIG. 18B, a first neural recordingsignal (320) is obtained at a stimulation current level of 350 μA. Thisneural recording signal (320) is identified as a non-response. Thestimulation current level is then increased to 400 μA and a secondneural recording signal (321) is obtained. This neural recording signal(321) is also identified as a non-response. This process is repeateduntil at least two non-responses (NR_(min)) are obtained. As shown inFIG. 18A, five non-responses (320-324) are obtained before a neuralresponse (325) is identified. If the neural response (325) is obtainedprior to obtaining the minimum number of non-responses NR_(min), thecurrent may be decreased to a level below 350 μA to obtain the desirednumber of non-responses.

As shown in FIG. 18A, the first neural response (325) is identified at acurrent level of 600 μA. The amplitude of the first neural response(325) detected is often relatively small and additional neural responsesignals may have to be obtained at this current level and averaged todetermine whether a neural response is actually present. Hence, as shownin FIG. 18B, after reaching the maximum allowable stimulation currentlevel of 750 μA, the stimulation current may be decreased to 600 μAwhere additional neural recording signals are taken and averaged until aneural response is detected.

FIG. 18A shows that neural responses are identified at current levels of600, 650, 700, and 750 μA. Hence, the neural recording signals (325-328)have been identified as including neural response signals.

Once the minimum number of neural responses R_(min) and minimum numberof non-responses NR_(min) have been obtained, as illustrated inconnection with FIGS. 18A and 18B, the neural response threshold currentmay be determined by analyzing the identified neural responses andnon-responses (step 313; FIG. 17). FIG. 19 illustrates an exemplaryanalysis of the neural responses and the non-responses that may be usedto determine the neural response threshold current. The peak-to-peakamplitudes of the neural recording signals (320-328; FIG. 18A) obtainedat each stimulus current level in the example described in connectionwith FIGS. 18A and 18B are plotted in the graph of FIG. 19. The unfilledpoints in the graph correspond to the non-responses (320-324; FIG. 18A)and the filled points correspond to the neural responses (325-328; FIG.18A).

As shown in FIG. 19, a closest-fit line (340) may be fit to a number ofthe points corresponding to the neural responses (325-328; FIG. 18A) andnon-responses (320-324; FIG. 18A) to analyze a trend in the datarepresented by the points. This closest-fit line (340) is referred to asa growth curve or contour.

The region (341) of the growth curve (340) between the higheststimulation current (342) corresponding to a non-response and the loweststimulation current (343) corresponding to a neural response may beanalyzed to determine the neural response threshold current level. Insome embodiments, the neural response threshold current level is equalto a value that falls within this region (341). The accuracy of theneural response threshold current level may be maximized by increasingthe number of signals averaged to obtain the responses andnon-responses, increasing the number of neural responses andnon-responses obtained, and/or decreasing the incremental step value ofthe stimulation currents from 50 μA to a smaller value.

The method illustrated in FIG. 17 of automatically determining a neuralresponse threshold may be performed by an application,processor-readable instructions, or the like that may be stored in aprocessor readable medium. The processor readable medium may be a harddrive, optical disc, or any other storage device medium.

The preceding description has been presented only to illustrate anddescribe embodiments of the invention. It is not intended to beexhaustive or to limit the invention to any precise form disclosed. Manymodifications and variations are possible in light of the aboveteaching.

1. A method of automatically determining a neural response thresholdcurrent level, said method comprising: obtaining a plurality of neuralrecording signals at a plurality of different stimulation currentlevels; fitting an artifact model to each of said neural recordingsignals to obtain a plurality of fitted artifact model signals;denoising each of said neural recording signals and each of said fittedartifact model signals; computing a strength-of-response metric for eachof said neural recording signals, said computation being based on atleast one of said denoised neural recording signals and at least one ofsaid fitted artifact model signals; comparing each of saidstrength-of-response metrics to a strength-of-response threshold toidentify one or more neural response signals and one or morenon-response signals contained within said neural recording signals; andanalyzing a trend between said neural response signals and saidnon-response signals to determine said neural response threshold currentlevel.
 2. The method of claim 1, wherein each of said neural responsesignals and said non-response signals corresponds to one of saidstimulation current levels and wherein said neural response thresholdcurrent level is substantially equal to a current level located inbetween a highest current level out of said stimulation current levelscorresponding to said non-response signals and a lowest current levelout of said stimulation current levels corresponding to said neuralresponse signals.
 3. The method of claim 1, further comprisingidentifying at least four neural response signals contained within saidneural recording signals.
 4. The method of claim 1, further comprisingidentifying at least two non-response signals within said neuralrecording signals.
 5. The method of claim 1, wherein a higheststimulation current level out of said stimulation current levelscorresponding to said neural response signals is less than orsubstantially equal to a maximum allowable current level.
 6. The methodof claim 1, wherein said step of analyzing said trend between saidneural response signals and said non-response signals comprisesgenerating and analyzing a growth curve corresponding to amplitudes ofsaid neural response signals and said non-response signals.
 7. Themethod of claim 1, further comprising: computing a net confidenceinterval for each of said neural recording signals, said computationbased on said denoised neural recording signals and said denoised fittedartifact model signals; and basing said computation of saidstrength-of-response metric on said net confidence intervals.
 8. Asystem for automatically determining a neural response threshold currentlevel, said system comprising: a stimulator configured to obtain aplurality of neural recording signals at a plurality of differentstimulation current levels; and one or more devices communicativelycoupled to said stimulator and configured to fit an artifact model toeach of said neural recording signals to obtain a plurality of fittedartifact model signals; denoise each of said neural recording signalsand each of said fitted artifact model signals; compute astrength-of-response metric for each of said neural recording signals,said computation being based on at least one of said denoised neuralrecording signals and at least one of said fitted artifact modelsignals; compare each of said strength-of-response metrics to astrength-of-response threshold to identify one or more neural responsesignals and one or more non-response signals contained within saidneural recording signals; and analyze a trend between said neuralresponse signals and said non-response signals to determine said neuralresponse threshold current level.
 9. The system of claim 8, wherein eachof said neural response signals and said non-response signalscorresponds to one of said stimulation current levels and wherein saidneural response threshold current level is substantially equal to acurrent level located in between a highest current level out of saidstimulation current levels corresponding to said non-response signalsand a lowest current level out of said stimulation current levelscorresponding to said neural response signals.
 10. The system of claim8, wherein said one or more devices are further configured to identifyat least four neural response signals contained within said neuralrecording signals.
 11. The system of claim 8, wherein said one or moredevices are further configured to identify at least two non-responsesignals within said neural recording signals.
 12. The system of claim 8,wherein a highest stimulation current level out of said stimulationcurrent levels corresponding to said neural response signals is lessthan or substantially equal to a maximum allowable current level. 13.The system of claim 8, wherein said one or more devices are furtherconfigured to generate and analyze a growth curve corresponding toamplitudes of said neural response signals and said non-response signalsto determine said neural response threshold current.
 14. The system ofclaim 8, wherein said one or more devices comprises at least one or moreof a computer, digital signal processor, and software application. 15.The system of claim 8, wherein said one or more devices are furtherconfigured to: compute a net confidence interval for each of said neuralrecording signals, said computation based on said denoised neuralrecording signals and said denoised fitted artifact model signals; andbase said computation of said strength-of-response metric on said netconfidence intervals.
 16. A system for automatically determining aneural response threshold current level, said system comprising: meansfor obtaining a plurality of neural recording signals at a plurality ofdifferent stimulation current levels; means for fitting an artifactmodel to each of said neural recording signals to obtain a plurality offitted artifact model signals; means for denoising each of said neuralrecording signals and each of said fitted artifact model signals; meansfor computing a strength-of-response metric for each of said neuralrecording signals, said computation being based on at least one of saiddenoised neural recording signals and at least one of said fittedartifact model signals; means for comparing each of saidstrength-of-response metrics to a strength-of-response threshold toidentify one or more neural response signals and one or morenon-response signals contained within said neural recording signals; andmeans for analyzing a trend between said neural response signals andsaid non-response signals to determine said neural response thresholdcurrent level.
 17. The system of claim 16, wherein each of said neuralresponse signals and said non-response signals corresponds to one ofsaid stimulation current levels and wherein said neural responsethreshold current level is substantially equal to a current levellocated in between a highest current level out of said stimulationcurrent levels corresponding to said non-response signals and a lowestcurrent level out of said stimulation current levels corresponding tosaid neural response signals.
 18. The system of claim 16, wherein ahighest stimulation current level out of said stimulation current levelscorresponding to said neural response signals is less than orsubstantially equal to a maximum allowable current level.
 19. The systemof claim 16, wherein said means for analyzing said trend between saidneural response signals and said non-response signals comprises meansfor generating and analyzing a growth curve corresponding to amplitudesof said neural response signals and said non-response signals.
 20. Thesystem of claim 16, further comprising: means for computing a netconfidence interval for each of said neural recording signals, saidcomputation based on said denoised neural recording signals and saiddenoised fitted artifact model signals; and means for basing saidcomputation of said strength-of-response metric on said net confidenceintervals.